"""Main module."""


# Web Scraping Module: This module can contain functions/classes for scraping text data from websites. You can use a library like BeautifulSoup or Scrapy for this purpose
# Preprocessing Module: This module can contain functions for common text preprocessing tasks such as tokenization, lowercasing, removing punctuation, stopword removal, and stemming/lemmatization.
# Feature Extraction Module: This module can include functions for extracting features from text, such as bag-of-words, TF-IDF, word embeddings, and more advanced methods like word2vec or BERT embeddings.
# Analysis Module: This module can contain functions for analyzing text, such as sentiment analysis, topic modeling (e.g., LDA), named entity recognition (NER), and text summarization.
# Classification Module: This module can include functions for text classification tasks, such as building and training classifiers using machine learning algorithms like Naive Bayes, SVM, or neural networks.
# Utility Functions: This module can contain utility functions that are useful for text processing tasks, such as loading and saving data, handling text formats, and evaluating models.
# Visualization Module: This module can include functions for visualizing text data and analysis results, such as word clouds, topic distributions, or sentiment histograms.
# Integration Module: This module can include functions or classes for integrating your package with other NLP libraries or services, such as spaCy, NLTK, or transformers for BERT.
